Jeffrey L. Anderson

Research Outline

(References can be found in the Curriculum
Vitae) This was written for a purpose that required writing from the third person perspective.

Overview

Anderson's research career has spanned two decades and has been focused by the
common theme to improve predictions of the earths atmosphere. He has made
research contributions in theoretical geophysical fluid dynamics (4, 6),
seasonal prediction (21, 24), predictability (14, 15), ensemble prediction (13,
17) and ensemble data assimilation (32, 44). His accomplishments in software
engineering, applied mathematics and statistics, while important on their own,
have been directly in support of his goal to improve prediction. His software
engineering contributions are described after his fundamental science work.

Stability of atmospheric flows

Andersons dissertation research concerned instability of the
atmospheric flows. This work hypothesized that low-frequency atmospheric
variability could be explained by looking at the linear instability of
barotropic dynamics linearized around observed mid-tropospheric time mean flows.
The most unstable modes strongly resembled low-frequency variability patterns
including some types of blocking and certain atmospheric teleconnections. The
thesis work led to several publications (3, 4) that were an important part of an
evolving literature on instability of non-zonal flows. Anderson developed novel
numerical methods for finding the most unstable modes of very large generalized
eigenvalue problems. These methods were essential to find unstable modes of
large atmospheric models and were later adopted and generalized by applied math
researchers.

Stationary states and blocking

In the early 1990s several researchers had explored the
possibility that persistent states of the atmosphere might be related to special
solutions of atmospheric models that had very small time tendencies. Anderson
developed high-dimensional minimization algorithms, later applied in
optimization for variational data assimilation, that could find states of large
models with very small time tendencies. These states were characterized by
special locally linear relations between vorticity and stream function. When
minimizations were begun from observed atmospheric states with persistent
blocking patterns, nearly zonal flows with imbedded high-amplitude modon-like
structures in the blocked region resulted (69). These modons had a linear
relation between vorticity and stream function that was distinct from that found
elsewhere at the same latitude. Several papers resulted from this research (5,
6) and argued that the persistence of blocking could be attributed to the
existence of modon-like atmospheric states with locally small time tendency (64,
65). This work led to a three month visit to KNMI in the Netherlands where
Anderson began collaborations on seasonal prediction and ensemble prediction.

Predictability on medium and seasonal timescales

The theoretical work on stability and stationary states was used
to explore predictability and prediction on timescales ranging from a week to
several seasons. Anderson used extended integrations from both atmospheric and
coupled general circulation models in an attempt to improve prediction
capabilities. In the early 1990s, the concept of return of skill was becoming
important in the use of GCMs for seasonal prediction. Anderson explored this
concept using monthly forecasts from the NCEP operational seasonal prediction
model (7, 8, 66). Using a newly developed diagnostic test for detecting blocking
events (9), he demonstrated that the climate of the GCM as a function of lead
time evolved from being similar to the observed climate to the long-term model
climate over a period of nearly a month. Significantly, the evolution was not
smooth. Instead, the model climate became relatively zonal during the first 10
days and then regenerated high- and medium-frequency variability consistent with
the model climatology during the next 10 days. This research indicated a need to
make model climates more consistent with the observed climate before they could
be used for medium range prediction (11, 16, 28). This work had a significant
impact on the direction of seasonal prediction GCMs at NCEP and NOAA research
laboratories.

In the mid-1990s , there was growing use of numerical
models for seasonal forecasts with an emphasis on the development of large GCMs,
both atmospheric and coupled, for prediction (23). Unfortunately, the actual
skill of these models were lower than prior expectations. Anderson obtained
output from operational seasonal prediction GCMs and simple statistical models
and demonstrated that the GCMs were not competitive with statistical methods
(21). In a seemingly counterintuitive result, he also demonstrated that simple
linear statistics were able to better predict the behavior of atmospheric GCMs
than were the GCMs themselves with available computing resources. These results
have held up to the present time and continue to signal caution that large model
development efforts are not always the best way to make progress on atmospheric
prediction at all time and space scales. Follow-up work led to methods for
statistically reducing error in GCM output to improve forecast skill (25).

Numerical prediction of tropical storm frequency

While GCMs have distinct limitations, they can also have some
powerful capabilities. While working with seasonal prediction models, Anderson
noticed that certain convective parameterizations could produce vortices that
were reminiscent of tropical storms. In low-resolution GCMs, these vortices were
far larger than real tropical storms, but had tracks and frequencies that were
similar to those for real storms (72). With graduate student Frederic Vitart,
Anderson explored these capabilities and their implications for seasonal
prediction of tropical storms (15, 26, 29). The work also led to conjectures
about climate model capabilities to predict changes in tropical storm frequency
and location in the presence of climate change (20). This work led to
operational GCM-based seasonal tropical storm prediction systems at NCEP and
later at ECMWF. Vitart has extended this work and now leads a seasonal
prediction effort at ECMWF. Andersons expertise on this area led to invitations
to speak at seasonal prediction centers around the world and his participation
in workshops at Bermudas Risk Prevention Initiative (91).

Ensemble prediction and ensemble tools

Anderson became one of the earliest researchers using model
ensembles for prediction in attacking the seasonal forecast problem (67, 68,
78). Initial research on this problem focused on so-called AMIP ensembles in
which an ensemble of AGCM integrations are forced by observed SSTs. The results
were interpreted as providing an upper bound on the predictability of the
atmosphere since SST forecasts themselves would be needed (12, 22, 71). This
research later included ensemble predictions with the fully-coupled
atmosphere/ocean/land model developed by Andersons group at GFDL (24, 73, 74).
Andersons group produced the first results on predictability in coupled
ocean-atmosphere models and he also published some of the earliest work looking
for seasonal predictability from land surface intial conditions (30, 80, 87).
Anderson led the development of tools for interpreting ensemble simulations and
prediction while at GFDL. Tools developed included the ranked histogram that
became the most widely applied tool for evaluating the quality of ensembles (13,
71). He also pioneered the use of non-parametric statistics such as the
Kolmogorov-Smirnov test for detecting statistically significant differences in
ensembles (12, 70). These tools are fundamental attempts to detect predictable
patterns in ensembles for all types of applications. Tools for detecting the
most predictable patterns from ensembles were also developed in collaboration
with postdocs at GFDL (18). Anderson participated in several national and
international groups that were exploring the potential skill of seasonal
prediction (23) during the late 1990s and has served on a variety of review and
advisory panels in this field.

Ensemble data assimilation

Exploration of ensemble prediction and predictability quickly
demonstrated that initial conditions for ensemble members were crucial to the
results. Anderson began to explore methods for generating appropriate initial
conditions in the mid-1990s at the same time as similar research was begun at
operational prediction centers. While the prediction centers generated heuristic
methods for ensemble generation and pushed them into operations, Anderson was
the first to point out that dynamical constraints were essential to producing
good ensemble predictions (10). His work was also among the early suggestions
that ensemble forecasts should be a random sample from the underlying
conditional distribution (14, 17). In the last decade, Anderson has become a
leader in ensemble data assimilation. The first correctly derived ensemble
filter algorithms were developed in Europe in 1998. Shortly after that time,
Anderson produced the first ensemble assimilation algorithms derived directly
from Bayes theorem without the need for intermediate reliance on the Kalman
filter (19, 90). He also introduced the use of kernel methods for producing
non-linear, non-Gaussian ensemble data assimilation (85, 90). To date, these
non-linear methods have proved too costly for operational atmospheric prediction
models but further enhancements in efficiency may yet allow them to become
ensemble assimilation tools of choice.Andersons insight into reducing the
statistical calculations to a sequence of scalar updating steps has proven to be
an important strategy for improving data assimilation algorithms and
implementing them for parallel computation (32,34).

To avoid problems of
filter divergence that plagued ensemble filters, Anderson also made early
contributions to the inflation method for dealing with model and assimilation
system error (27). Inflation has become, in various forms, a central component
of all ensemble assimilation systems in large models.

Adaptive methods to support a generic research testbed

By 2002, ensemble filters were being widely applied, but they
still required expert knowledge for localization (33). Traditionally,
localization in ensemble filters has restricted the impact of observations to
some set of physically adjacent state variables. Both expert knowledge about
models, observations, and ensemble data assimilation and a large amount of
tuning are needed to find appropriate localizations for good assimilation
performance. Anderson realized that localization could instead be reformulated
as a response to sampling error in ensemble assimilation. He developed a method
to automatically determine appropriate localizations using ensembles of
ensembles. This method, the hierarchical filter, can help model developers or
observationalists to use ensemble data assimilation systems effectively without
developing expertise on ensemble filtering theory (45). The hierarchical filter
also demonstrates that physical distance is not the appropriate measure for
localization. Instead, an information or correlation distance is more
appropriate. Observations should be allowed to influence state variables when
the ensemble system is able to identify significant sample correlations between
the state variable and observation prior ensembles. This leads to much more
powerful types of localization and adapts to more complicated relationships
among the state variables. Proper localization of this type can greatly increase
the quality of ensemble assimilations in prediction models.

In order to
support the use of ensemble assimilation facilities across a variety of
different models and physical systems it is also necessary to improve on the
heuristic inflation algorithm. Recently, Anderson has developed hierarchical
Bayesian techniques to detect model and assimilation system error (44). The same
observations that are used for the traditional part of the assimilation are also
used to detect inaccuracies in the prior ensemble variance. Bayes theorem is
then used to adjust the ensemble variance. Initial implementations of this
adaptive inflation remove the need for the tuning of the inflation factor. Work
in press describes adaptive inflation algorithms that are even more powerful.
These algorithms detect variance errors for each model state vector component
and can correct for model and assimilation system errors that are a function of
space, variable type, time, or all three (50, 51).

Anderson's current
research continues to lead the ensemble data assimilation field (55). He is
exploring ways to detect observation system errors at the same time as model
error. Methods for reducing ensemble size while maintaining assimilation quality
are also under development. Tracer assimilation and ensemble smoothing (47), in
which future observations are used to generate state estimates are also work in
progress.

The Flexible Modeling System

From 1992-2002, Anderson worked on the development of the
Flexible Modeling System (FMS) at GFDL (62). FMS is a set of tools and a
software infrastructure to support modeling of the coupled ocean/atmosphere/land
system. While it proved relatively easy to develop tools, it was extremely
difficult to convince researchers with existing models to adopt the tools.
Becoming the head of the GFDL Experimental Prediction Group gave Anderson an
opportunity to push forward by first developing an atmospheric model based on
FMS and then a fully coupled model. Along the way, the concept of the 'exchange
grid' for coupling component models was developed (38) and implemented in a
highly-scalable fashion. Performance of the FMS-based models was so good that
other modeling groups at GFDL were eventually compelled to adopt FMS. The
resulting enhancements in efficiency and collaboration greatly accelerated
progress at GFDL and were essential in GFDL being able to participate in the
last two IPCC reports. Anderson's expertise on object-oriented modeling led to
his participation in a sequence of panels exploring coding requirements for
atmospheric modeling. The OSTP/USGCRP advisory panel on climate modeling, that
generated the so-called 'Rood report', made recommendations about software
engineering practices that were required to improve atmospheric modeling
capabilities. Anderson was involved in a number of follow-on meetings that led
to the initial ESMF proposal to NASA (31). FMS served as one of two prototypes
for the initial ESMF effort. In particular, the FMS time_manager and the
exchange grid capability was direct ancestors of central ESMF capabilities. FMS
continues to support modeling at GFDL and is still providing insight for ESMF
developers. The development of sound software infrastructure for atmospheric
modeling has greatly accelerated scientific advances.

When Anderson took over the GFDL seasonal prediction group, a
legacy spectral model was being used for research. From 1992 on, Anderson led an
effort to develop more modern prediction models using the developing FMS
framework. The goal was to have efficient models that could easily use a variety
of different modular physical parameterizations. Under Anderson's leadership,
the E-grid global model developed by Messinger was converted into a B-grid
dynamical core. This is the dynamical core that is in the DART facility and is
used as in data assimilation research by a number of research groups (e.g. 37).
At the same time, a modular interface between dynamics and physics was developed
along with versions of a number of physics packages. By 1998, the resulting GCM
was being used for seasonal prediction at GFDL with particular focus on
extratropical predictability and predictability of tropical storm frequency (70,
72, 76). As GFDL prepared for the next IPCC cycle, this model was adopted by the
climate community at GFDL and became the AM model series (39) used in IPCC and
is still in use at GFDL.

The Bgrid core was also coupled to the MOM3
ocean model under Andersons leadership using the prototype FMS exchange grid to
form a coupled modular seasonal prediction model (73, 74, 80). This model
evolved into the GFDL CM model series after Anderson's move to NCAR, but the
underlying software framework has remained the same.

The Data Assimilation Research Testbed

The success of the object-oriented software tools developed for
FMS and ESMF, along with the development of ensemble filtering algorithms for
data assimilation, raised the possibility of developing a generic facility for
ensemble data assimilation. In 2001, Anderson began development of the Data
Assimilation Research Testbed (63). The goal was to build a data assimilation
facility that could be applied to a wide range of models and observational
datasets without requiring users to have any special data assimilation
expertise. It was vital that very little coding be required to incorporate
models or observations into the facility and that it run efficiently on a wide
range of computing platforms (43). The key software engineering aspects of DART
have been the design of interfaces between the assimilation facility and models
or observations. The modularity of the functions in DART allows a modeler to
focus on model improvements in the context of prediction without having to
develop a companion data assimilation system. Conversely, DART can support
research on data assimilation algorithms or new types of observations without
requiring that the researcher have detailed knowledge of calling a numerical
model. Although DART is computationally efficent and often gives skillful
predictions compared to larger operational systems, it also gives university
researchers, students and other small teams access to conduct state-of-the-art
research in prediction and data assimilation. A number of models have been
incorporated into DART including nearly a dozen large models like GCMs. DART is
now being used at more than a dozen UCAR member universities, at government labs
and prediction centers, and in private industry (Table 1).

The
availablility of DART has facilitated scientific research on a wide range of
problems. Although many of the collaborations that Anderson facilitates do not
lead to coauthorship on publications, he is central to several activities at
NCAR. Research on fundamental questions about the application of ensemble
filters to prediction models have improved prediction (33, 35). Seasonal
prediction in both low-order models (46) and coupled GCMs (36) has been a focus.
Exploring the value of existing observations (37, 41, 40) or using new
observation types such as GPS radio occultation (42, 48) has led to better uses
of a variety of observations. Anderson has actively participated in the use of
DART in several NCAR models including CAM (52, 53), WRF (48, 41), and middle
atmosphere models (49).